System Description for a Scalable, Fault-Tolerant, Distributed Garbage Collector

We describe an efficient and fault-tolerant algorithm for distributed cyclic garbage collection. The algorithm imposes few requirements on the local machines and allows for flexibility in the choice of local collector and distributed acyclic garbage collector to use with it. We have emphasized reducing the number and size of network messages without sacrificing the promptness of collection throughout the algorithm. Our proposed collector is a variant of back tracing to avoid extensive synchronization between machines. We have added an explicit forward tracing stage to the standard back tracing stage and designed a tuned heuristic to reduce the total amount of work done by the collector. Of particular note is the development of fault-tolerant cooperation between traces and a heuristic that aggressively reduces the set of suspect objects.Comment: 47 pages, LaTe

An efficient distributed algorithm for computing minimal hitting sets

Computing minimal hitting sets for a collection of sets is an important problem in many domains (e.g., Spectrum-based Fault Localization). Being an NP-Hard problem, exhaustive algorithms are usually prohibitive for real-world, often large, problems. In practice, the usage of heuristic based approaches trade-off completeness for time efficiency. An example of such heuristic approaches is STACCATO, which was proposed in the context of reasoning-based fault localization. In this paper, we propose an efficient distributed algorithm, dubbed MHS2, that renders the sequential search algorithm STACCATO suitable to distributed, Map-Reduce environments. The results show that MHS2 scales to larger systems (when compared to STACCATO), while entailing either marginal or small run time overhead

Local cohomology and stratification

We outline an algorithm to recover the canonical (or, coarsest) stratification of a given finite-dimensional regular CW complex into cohomology manifolds, each of which is a union of cells. The construction proceeds by iteratively localizing the poset of cells about a family of subposets; these subposets are in turn determined by a collection of cosheaves which capture variations in cohomology of cellular neighborhoods across the underlying complex. The result is a nested sequence of categories, each containing all the cells as its set of objects, with the property that two cells are isomorphic in the last category if and only if they lie in the same canonical stratum. The entire process is amenable to efficient distributed computation.Comment: Final version, published in Foundations of Computational Mathematic

Collaborative data collection scheme based on optimal clustering for wireless sensor networks

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication

Top-k sparsification has recently been widely used to reduce the communication volume in distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) dilemma, the performance of top-k sparsification still has limitations. Recently, a few methods have been put forward to handle the SGA dilemma. Regrettably, even the state-of-the-art method suffers from several drawbacks, e.g., it relies on an inefficient communication algorithm and requires extra transmission steps. Motivated by the limitations of existing methods, we propose a novel efficient sparse communication framework, called SparDL. Specifically, SparDL uses the Spar-Reduce-Scatter algorithm, which is based on an efficient Reduce-Scatter model, to handle the SGA dilemma without additional communication operations. Besides, to further reduce the latency cost and improve the efficiency of SparDL, we propose the Spar-All-Gather algorithm. Moreover, we propose the global residual collection algorithm to ensure fast convergence of model training. Finally, extensive experiments are conducted to validate the superiority of SparDL

Distributed Garbage Collection of Active Objects

This paper shows how to perform distributed automatic garbage collection of objects possessing their own thread of control. The relevance of garbage collection and concurrent objects used in the paper is explained. The collector is comprised of a collection of independent local collectors, one per node, loosely coupled to a distributed global collector. The mutator (application), the local collectors and the global collector run concurrently. The synchronization necessary to achieve correct and efficient concurrent operation between the collectors and the mutator is presented in detail. An interesting aspect of the distributed collector is the termination algorithm: the collector algorithm running on one node, which considers itself to be "done," may become "undone" by the action of a collector algorithm on another node

Performance of a hierarchical distributed garbage collection algorithm in ActorFoundry

Automatic garbage collection is an essential feature so that programs can reclaim resources without the need for manual input. This feature is present in many modern languages and is a common subject of research. However, in parallel and distributed environments, programmer-controlled resource reclamation is highly error-prone. As the scale of programs increase, automatic garbage collection is of paramount importance for efficient and error-free execution. Garbage collection in the context of actor systems is especially difficult because actors are active objects and may not be garbage even if there are no references to it. An additional difficulty is to perform garbage collection on active objects without halting the current computation. This thesis implements one of the proposed algorithms which can solve the problem of garbage collection in distributed actor systems. This study also explores how parameters in this algorithm along with how the topology of an actor system affect the garbage collection. This was implemented on an existing actor framework in order to highlight key factors in the algorithm's performance. The design details and insights gained from the results of these tests are then discussed

Travelling Salesman Problem using Prim Algorithm in High Performance Computing

Thanks to the advances in wide area network technology and the low cost of computing resources, High Performance Computing came into being and currently research area. One incentive of High Performance Computing is to summative the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal an efficient job scheduling algorithm system is an essential part of the High Performance Computing. This preliminary report emphasizes on the basic terms of the efficient job scheduling algorithm for traveling salesman problem in high performance computing. Job scheduling algorithm will reduce the traffic between the processors and can help improve resource utilization and quality of service. Traveling salesman problem is finding is the shortest path connecting number of locations such as cities, visited by a traveling salesman on his sales route. TSP has been used in The Two-Period Travelling Salesman Problem Applied to Milk Collection in Ireland and Usefulness of Solution Algorithms of the Travelling Salesman Problem in the typing of Biological Sequences in a Clinical Laboratory Setting